Data-driven parametric soliton-rogon state transitions for nonlinear wave equations using deep learning with Fourier neural operator

Ming Zhong, Zhenya Yan, Shou-Fu Tian

Communications in Theoretical Physics ›› 2023, Vol. 75 ›› Issue (2) : 25001.

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Communications in Theoretical Physics ›› 2023, Vol. 75 ›› Issue (2) : 25001. DOI: 10.1088/1572-9494/acab55
Mathematical Physics

Data-driven parametric soliton-rogon state transitions for nonlinear wave equations using deep learning with Fourier neural operator

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{{article.zuoZheEn_L}}. {{article.title_en}}[J]. {{journal.qiKanMingCheng_EN}}, 2023, 75(2): 25001 https://doi.org/10.1088/1572-9494/acab55

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